Shuffled Deep Regression
Abstract
Shuffled regression is the problem of learning regression models from shuffled data that consists of a set of input features and a set of target outputs where the correspondence between the input and output is unknown. This study proposes a new deep learning method for shuffled regression called Shuffled Deep Regression (SDR). We derive the sparse and stochastic variant of the Expectation-Maximization algorithm for SDR that iteratively updates discrete latent variables and the parameters of neural networks. The effectiveness of the proposal is confirmed by benchmark data experiments.
Cite
Text
Kohjima. "Shuffled Deep Regression." AAAI Conference on Artificial Intelligence, 2024. doi:10.1609/AAAI.V38I12.29224Markdown
[Kohjima. "Shuffled Deep Regression." AAAI Conference on Artificial Intelligence, 2024.](https://mlanthology.org/aaai/2024/kohjima2024aaai-shuffled/) doi:10.1609/AAAI.V38I12.29224BibTeX
@inproceedings{kohjima2024aaai-shuffled,
title = {{Shuffled Deep Regression}},
author = {Kohjima, Masahiro},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2024},
pages = {13238-13245},
doi = {10.1609/AAAI.V38I12.29224},
url = {https://mlanthology.org/aaai/2024/kohjima2024aaai-shuffled/}
}